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Classification of basil plant based on the level of consumed nitrogen fertilizer using an olfactory machine

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Abstract

Basil (Ocimum basilicum) is an aromatic plant with numerous medical properties that belongs to the Lamiaceae family. The application of nitrogen fertilizer has high impacts on the basil yield improvement. However, using a high amount of nitrogen fertilizer modulates plant metabolisms especially amino acid derivatives and other nitrogen-containing secondary metabolism. Nitrogen fertilizer also increases nitrate accumulation in plant tissues which is unsafe for human health. Therefore, the present study was carried out to classify the basil plant based on the different levels of consumed nitrogen fertilizer using a machine olfaction system (e-nose machine). To achieve this, four different levels of urea fertilizer (0, 50, 100, and 150 kg ha−1) were tested. The moisture content of samples was quite similar. Collected data were analyzed by principal component analysis, artificial neural networks, linear discriminant analysis, and quadratic discriminant analysis methods. According to the results, TGS822 sensor displayed the most efficient response for classification. Considering the results of PCA, 90% of data variance was covered by PC1 and PC2. Similarly, confusion matrixes obtained from ANN, LDA, and QDA analysis presented the accuracy of 96.7%, 95%, and 97.78%, respectively. The average values of accuracy, precision, sensitivity, specificity, and AUC parameters per class for the best model of QDA were 0.9891, 0.9638, 0.9638, 0.9937, and 0.9788, respectively.

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References

  • Al-mansour B, Kalaivanan DA, Suryanarayana M, Umesha KK, Nair A (2018) Influence of organic and inorganic fertilizers on yield and quality of sweet basil (Ocimumbasilicum L.). Journal of Spices and Aromatic Crops 27(1):38–44

    Google Scholar 

  • Arshak K, Moore E, Lyons G, Harris J, Clifford S (2004) A review of gas sensors employed in electronic nose applications. SeRv 24(2):181–198

    Google Scholar 

  • Ayari F, Mirzaee- Ghaleh E, Rabbani H, Heidarbeigi K (2018a) Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat). Int J Food Prop 21(1):1670–1679

    Article  CAS  Google Scholar 

  • Ayari F, Mirzaee- Ghaleh E, Rabbani H, Heidarbeigi K (2018) Using an e-nose machine for detection the adulteration of margarine in cow ghee. J Food Process Eng 41(6):e12806

    Article  Google Scholar 

  • Basri KN, Hussain MN, Bakar J, Sharif Z, Khir MFA, Zoolfakar AS (2107) Classification and quantification of palm oil adulteration via portable NIR spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 173:335–342

    Article  Google Scholar 

  • Carcano EC, Bartolini P, Muselli M, Piroddi L (2008) Jordan recent neural network versus IHACRES in modeling daily streamflows. J Hydrol 362:291–307

    Article  Google Scholar 

  • Cheng SM, Wang J, Wang YW, Wei ZB (2017) Discrimination of different types damage of tomato seedling by electronic nose. ITM Web of Conferences 11:01019

    Article  Google Scholar 

  • Donis-González IR, Guyer DE, Leiva-Valenzuela GA, Burns J (2012) Assessment of chestnut (Castanea spp.) slice quality using color images. J Food Eng 115:407–414

    Article  Google Scholar 

  • Eifler J, Martinelli E, Santonico M, Capuano R, Schild D (2011) Differential detection of potentially hazardous fusarium species in wheat grains by an electronic nose. PLos one 6(6):e21026

    Article  CAS  Google Scholar 

  • Falowo A B, Mukumbo F E, Idamokoro E M, Afolayan A J, Muchenje V (2019) Phytochemical constituents and antioxidant activity of sweet basil (Ocimum basilicum L.) essential oil on ground beef from boran and nguni cattle. International Journal of Food Science 2628747.

  • Ghasemi-Varnamkhasti M, Mohtasebi SS, Siadat M, Balasubramanian S (2009) Meat quality assessment by electronic nose (machine olfaction technology) Sensors 9: 6058–6083.

  • Ghasemi-Varnamkhasti M, Mohtasebi SS, Siadat M, LozanoJ AH, Razavi SH, Dicko A (2011) Aging fingerprint characterization of beer using electronic nose. Sens Actuators, B Chem 159:51–59

    Article  CAS  Google Scholar 

  • Gorji-Chakespari A, Nikbakht AM, Sefidkon F, Ghasemi-Varnamkhasti M, Valero EL (2017) Classification of essential oil composition in Rosa damascena Mill. genotypes using an electronic nose. J Appl Res Med Aromat Plants 4:27–34

    Google Scholar 

  • Haddi Z, Amari A, Ali AO, Bari NE, Barhoumi H, Maaref A, Jaffrezic-Renault N, Bouchikhi B (2011) Discrimination and identification of geographical origin virgin olive oil by an e-nose based on MOS sensors and pattern recognition techniques. Procedia Eng 25:1137–1140

    Article  CAS  Google Scholar 

  • Heaton J (2008) Introduction to neural networks for Java, 2nd Edition. Heaton Research, Inc.

  • Heidarbeigi K (2015) Implementation, fabrication and evaluation of a system for detecting original saffron from adulteration based on electronic tongue and electronic nose technology. Department of agricultural machinery engineering, University of Tehran, Faculty of agricultural engineering and technology ((In Persian))

    Google Scholar 

  • Heidarbeigi K, Mohtasebi SS, Foroughirad A, Ghasemi-Varnamkhasti M, Rafiee S, Rezaei K (2015) Detection of adulteration in saffron samples using electronic nose. Int J Food Prop 18(7):1391–1401

  • Hosseinzadeh-Samani B, Lorigooini Z, Fasihzadeh Sh (2017) Evaluation of the effect of different microwave dryer poweron the qualitative and quantitative properties of essential oil of bakhtiari savory. Iran J Food Sci Technol 66(14):145–155 ((In Persian))

    Google Scholar 

  • Karami H, Rasekh M, Mirzaee-Ghaleh E (2020a) Application of the e-nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation, n/a(n/a), e14696. https://doi.org/10.1111/jfpp.14696.

  • Karami H, Rasekh M, Mirzaee-Ghaleh E (2020b) Qualitative analysis of edible oil oxidation using an olfactory machine. J Food Measure Charact. https://doi.org/10.1007/s11694-020-00506-0

    Article  Google Scholar 

  • Karami H, Rasekh M, MirzaeeGhaleh E (2020) Comparison of chemometrics and AOCS official methods for predicting the shelf life of edible oil. Chemom Intell Lab Syst 206:104165

    Article  CAS  Google Scholar 

  • Kiani S, Minaei S, Ghasemi-Varnamkhasti M (2016) Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: a review. J Appl Res Med Aromat Plants 3:1–9

    Google Scholar 

  • Klougart A (1976) Urea as a nitrogen source for pot plants. Acta Hort 64:103–114

    Article  Google Scholar 

  • Li C, Heinemann P, Sherry R (2007) Neural network and bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sens Actuators, B Chem 125:301–310

    Article  CAS  Google Scholar 

  • Loutfi A, Coradeschi S, Mani GK, Shankar P, Rayappan JB (2015) Electronic noses for food quality: a review. J Food Eng 144:103–111

    Article  CAS  Google Scholar 

  • Mahmodi K, Mostafaei M, Mirzaee- Ghaleh E (2019) Detection and classification of diesel-biodiesel blends by LDA, QDA and SVM approaches using an electronic nose. Fuel 258:116114

    Article  CAS  Google Scholar 

  • Mahmoudi E (2009) Electronic nose technology and its applications. Sensors Transducers 107:17–25

    Google Scholar 

  • McLachlan G (2004) Discriminant analysis and statistical pattern recognition. John Wiley and Sons

  • Mendes RT, Resende RC, Pereia MAM, Bento RU, Dias da Silva RC, Cruz SJS, Pela A (2016) Foliar application of urea and bell pepper amino acids. Afr J Agric Res 11(19):1674–1678

    Article  CAS  Google Scholar 

  • Mirzaee- Ghaleh E, Taheri-Garavend A, Ayari F, Lozano J (2019) Identification of fresh-chilled and frozen-thawed chicken neat and estimation of their shelf life using an e-nose machine coupled fuzzy KNN. Food Anal Methods 13:678–689

    Article  Google Scholar 

  • Mohammadi V, Kheiralipour K, Ghasemi-Varnamkhasti M (2015) Detecting maturity of persimmon fruit based on image processing technique. Sci Hortic 184:123–128

    Article  Google Scholar 

  • Neapolitan R E, Jiang X (2012) Contemporary artificial intelligence: CRC Press

  • Oliveros MCC, Pavón JLP, Pinto CG, Laespada MEF, Cordero BM, Forina M (2002) Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils. Anal Chim Acta 459(2):219–228

    Article  Google Scholar 

  • Olsson J, Borjesson T, Lundstedt T, Schnurer J (2002) Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC MS and electronic nose. Int J Food Microbiol 72:203–214

    Article  CAS  Google Scholar 

  • Omid M, Mahmoudi A, Omid MH (2010) Development of pistachio sorting system using principal component analysis (PCA) assisted artificial neural network (ANN) of impact acoustics. Expert Syst Appl 37:7205–7212

    Article  Google Scholar 

  • Rahman S, Usmani T, Saeed S H (2013) Review of electronic nose and application. International Journal of Computing and Corporate Research 3 (2).

  • Rutolo MF, Iliescu D, Clarkson GP, Covington JA (2016) Early identification of potato storage disease using an array of metaloxide based gas sensors. Postharvest Biol Technol 116:50–58

    Article  CAS  Google Scholar 

  • Sanaeifar A, Mohtasebi SS, Ghasemi-Varnamkhasti M, Ahmadi H (2016) Application of MOS based electronic nose for the prediction of banana quality properties. Measurement 82:105–114

    Article  Google Scholar 

  • Shrifi R, Ryu CM (2018) Sniffing bacterial volatile compounds for healthier plants. Curr Opin Plant Biol 44:88–97

    Article  Google Scholar 

  • Sharifi R, Ryu C (2018) Biogenic volatile compounds for plant disease diagnosis and health improvement. Plant Pathol J 34:459–469

    Article  CAS  Google Scholar 

  • Sifola M L, Barbieri G (2006) Growth, yield and essential oil content of three cultivars of basil grown under different levels of nitrogen in the field. Scientia Horticulturae108 (4): 408- 413

  • Taheri- Gravand A, Ahmadi H, Omid M, Mohtasebi SS, Mollazade K, Smith AJR, Carlomagno GM (2015) An intelligent approach for cooling radiator diagnosis based on infrared thermal image processing technique. Appl Therm Eng 87:434–443

    Article  Google Scholar 

  • Tohidi M, Ghasemi-Varnamkhasti M, Ghafarinia V, Bonyadian M, Mohtasebi SS (2018) Development of a metal oxide semiconductor-based artificial nose as a fast, reliable and non-expensive analytical technique for aroma profiling of milk adulteration. Int Dairy J 77:38–46

    Article  CAS  Google Scholar 

  • Upadhyay N (2014) Detection of vegetable oil and animal body fat adulteration in ghee using solvent fractionation technique. Thesis doctor of philosophy in dairying (Dairy Chemistry). National Dairy Research Institute, Karnal (Deemed University), India

  • Vesali F, Omid M, Kaleita A, Mobli H (2015) Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Comput Electron Agric 116:211–220

    Article  Google Scholar 

  • Wilson A D (2016) Identification and discrimination of herbicide residue using a conducting polymer electronic nose. In Proceedings of the VII International Conference on Sensor Device Technologies and Applications, Nice, France, 24–28 July, International Academy, Research, and Industry Association (IARIA): Wilmington, DE, USA: pp. 4–7.

  • Wilson A D (2013) Fungicide residue identification and discrimination using a conducting polymer electronic-nose. In Proceedings of the IV International Conference on Sensor Device Technologies and Applications, Barcelona, Spain, 25–31 August, Yurish, S., Chilibon, I., Carvalho, V., Gervais-Ducouret, S., Eds.; Xpert Publishing Services: Wilmington, DE, USA, pp. 116–121

  • Xu S, Zhou Z, Lu H, Luo X, Lan Y, Zhang Y, Li Y (2014) Estimation of the age and amount of brown rice plant hoppers based on bionic electronic nose use. Sensors 14:18114–18130

    Article  Google Scholar 

  • Zhang F, Iliescu D D, Hines E L, Leeson M S (2011) Tomato plant health monitoring: an electronic nose approach. In Intelligent Systems for Machine Olfaction Tools and Methodologies: 231–248.

  • Zohora SE, Khan AM, Srivastava AK, Hundewale N (2013) Electronic noses application to food analysis using metal oxide sensors: a review. Int J Soft Comput Eng 3:199–205

    Google Scholar 

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Acknowledgements

The financial support provided by the Razi University is duly acknowledged.

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Authors and Affiliations

  1. Mechanical Engineering of Biosystems Department, Razi University, Kermanshah, Iran

    Faraneh Khodamoradi, Esmaeil Mirzaee-Ghaleh & Mohammad Jafar Dalvand

  2. Department of Plant Protection , Razi University, Kermanshah, Iran

    Rouhallah Sharifi

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  1. Faraneh Khodamoradi
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  2. Esmaeil Mirzaee-Ghaleh
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  3. Mohammad Jafar Dalvand
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  4. Rouhallah Sharifi
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Correspondence to Esmaeil Mirzaee-Ghaleh.

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Faraneh Khodamoradi declares that she has no conflict of interest. Esmaeil Mirzaee-Ghaleh declares that he has no conflict of interest. Mohammad Jafar Dalvand declares that he has no conflict of interest. Rouhallah Sharifi declares that he has no conflict of interest.

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Khodamoradi, F., Mirzaee-Ghaleh, E., Dalvand, M.J. et al. Classification of basil plant based on the level of consumed nitrogen fertilizer using an olfactory machine. Food Anal. Methods 14, 2617–2629 (2021). https://doi.org/10.1007/s12161-021-02089-y

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